References

W​eek 1:

• The Sequential model (TensorFlow Documentation)
• The Functional API (TensorFlow Documentation)

W​eek 2:

• Deep Residual Learning for Image Recognition (He, Zhang, Ren & Sun, 2015)
• d​eep-learning-models/resnet50.py/ (GitHub: fchollet)
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (Howard, Zhu, Chen, Kalenichenko, Wang, Weyand, Andreetto, ​& Adam, 2017)
• MobileNetV2: Inverted Residuals and Linear Bottlenecks (Sandler, Howard, Zhu, Zhmoginov &Chen, 2018)
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (Tan & Le, 2019)

W​eek 3:

• You Only Look Once: Unified, Real-Time Object Detection (Redmon, Divvala, Girshick & Farhadi, 2015)
• YOLO9000: Better, Faster, Stronger (Redmon & Farhadi, 2016)
• YAD2K (GitHub: allanzelener)
• YOLO: Real-Time Object Detection
• Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs (Novikov, Lenis, Major, Hladůvka, Wimmer & Bühler, 2017)
• Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks (Dong, Yang, Liu, Mo & Guo, 2017)
• U-Net: Convolutional Networks for Biomedical Image Segmentation (Ronneberger, Fischer & Brox, 2015)

W​eek 4:

• FaceNet: A Unified Embedding for Face Recognition and Clustering (Schroff, Kalenichenko & Philbin, 2015)
• DeepFace: Closing the Gap to Human-Level Performance in Face Verification (Taigman, Yang, Ranzato & Wolf)
• f​acenet (GitHub: davidsandberg)
• How to Develop a Face Recognition System Using FaceNet in Keras (Jason Brownlee, 2019)
• k​eras-facenet/notebook/tf_to_keras.ipynb (GitHub: nyoki-mtl)
• A Neural Algorithm of Artistic Style (Gatys, Ecker & Bethge, 2015)
• Convolutional neural networks for artistic style transfer
• TensorFlow Implementation of "A Neural Algorithm of Artistic Style"
• V​ery Deep Convolutional Networks For Large-Scale Image Recognition (Simonyan & Zisserman, 2015)
• Pretrained models (MatConvNet)